Perception-Based Speed Limit Estimation And Learning

ABSTRACT

Systems, methods, and devices for estimating a speed limit are disclosed herein. A system for estimating a speed limit includes one or more perception sensors, an attribute component, an estimator component, and a notification component. The one or more perception sensors are configured to generate perception data about a region near a vehicle. The attribute component is configured to detect one or more environmental attributes based on the perception data. The estimator component is configured to determine an estimated speed limit based on the environmental attributes. The notification component is configured to provide the estimated speed limit to an automated driving system or driver assistance system of the vehicle.

TECHNICAL FIELD

The disclosure relates generally to methods, systems, and apparatusesfor automated driving or for assisting a driver, and more particularlyrelates to methods, systems, and apparatuses for determining orestimating a speed limit.

BACKGROUND

Automobiles provide a significant portion of transportation forcommercial, government, and private entities. Due to the high cost andvalue of automobiles and potential harm to passengers and drivers,driver safety and avoidance of collisions or accidents are extremelyimportant. In order to increase safety and reduce risk of propertydamage many roads have speed limits for vehicles, which may be enforcedby law enforcement organizations.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive implementations of the presentdisclosure are described with reference to the following figures,wherein like reference numerals refer to like parts throughout thevarious views unless otherwise specified. Advantages of the presentdisclosure will become better understood with regard to the followingdescription and accompanying drawings where:

FIG. 1 is a schematic block diagram illustrating an implementation of avehicle control system that includes an automated driving/assistancesystem;

FIG. 2 illustrates a perspective view of an example road environment;

FIG. 3 illustrates a perspective view of another example roadenvironment;

FIG. 4 illustrates a perspective view of yet another example roadenvironment;

FIG. 5 is a schematic diagram illustrating a top view of an example roadenvironment;

FIG. 6 is a schematic block diagram illustrating example components of aspeed component, according to one implementation;

FIG. 7 is a schematic block diagram illustrating a model for speed limitestimation, according to one implementation;

FIG. 8 is a schematic block diagram illustrating a model for speed limitestimation and machine learning, according to one implementation;

FIG. 9 is a schematic block diagram illustrating a method fordetermining whether to use an estimated speed limit or an arbitratedspeed limit, according to one implementation; and

FIG. 10 is a schematic block diagram illustrating a method fordetermining a speed limit, according to one implementation.

DETAILED DESCRIPTION

Although speed limits are often present on roads, these legal speedlimits (or, simply, speed limits) are not always immediately apparent todrivers. As an example, consider that upon entering an interstatehighway, it can be several miles before one sees a speed limit sign. Asanother example, there may be no posted speed limit within a residentialarea, but a “town” speed limit may still be in effect. In these cases,human drivers may be expected to use judgment in estimating the speedlimit. In some cases, a vehicle may be controlled by an automateddriving system (or other “virtual driver”) that controls the vehicleusing sensors, which detect the environment. In some instances, speedlimit information may be available to the automated driving system orvirtual driver via a map, electronic horizon, or speed limit sign. Inothers, the automated driving system or virtual driver may be withoutspeed limit information, and may need to estimate a reasonable speedlimit. However, if a vehicle driving system or human driver incorrectlyestimates a speed limit, there may be an increased risk of an accidentor speed limit violation for the vehicle.

The present application discloses systems, methods, and devices forestimating a speed limit. In one embodiment, a speed limit may beestimated based on the presence of houses, a concrete median, lanemarkings, curbs, or rumble strips, a number of lanes, lane width, orother environmental attributes on or near a road. For example, theestimated speed limit may be used as a “sanity check” for a driver, oran automated driving system, to ensure that the vehicle is travelingapproximately at the correct speed. In one embodiment, an estimate for alegal speed limit can be determined by using perception sensors.Perception sensors are those which sense the environment such as acamera, radar system, light detection and ranging (LIDAR) system,ultrasound system, or any other image or ranging detection system. Forexample, a system may map instantaneous perception-sensor information toa reasonable speed limit. In a case where the perception sensor is acamera, the estimator processes one or more images from the camera todetermine a reasonable speed limit. For example, the object recognitionor pattern recognition may be performed on perception data to detect oridentify environmental attributes. In one embodiment, a neural networkor graphical model may be used to map speed values to pixels. Forexample, areas of space may be labeled as 35 miles per hour (MPH), 40MPH, 45 MPH, 55 MPH, etc. It will be appreciated that while UnitedStates primarily uses miles per hour, the disclosure is also applicableto metric speed units. Thus, the disclosure contemplates estimating andadjusting speed limits according to the location in which the vehiclemay be used and operated without departing from the scope of thedisclosure.

In one embodiment, estimation algorithms may be hard-coded or may belearned and modified using artificial intelligence. For example, theestimator may use a set of parameters which may be held constant or maybe updated based on a driving history or experience of a vehicle. In oneembodiment, an estimation system may implement an online learningalgorithm. For example, the learning algorithm may use ab estimatedoutput and a known speed limit to update parameters used to estimatespeed limits. The known speed limit may be obtained from a drivinghistory, map, speed, sign, or other source. In one embodiment, thelearning algorithm updates the estimation parameters when there is highconfidence in an arbitrated legal speed limit (e.g., based on a sign,map, or other source that provides a speed limit with a high level ofaccuracy). In one embodiment, a system or method may feed an imagethrough a neural network or a machine learning algorithm to learn whataspects of the environment correspond to a known speed limit. Forexample, the embodiments may be capable of training a controller orsystem to travel at a given speed without comparing that speedinformation to a database or other known data source, such as in a casewhere a GPS signal or system is non-functional or incorrect.

In the following disclosure, reference is made to the accompanyingdrawings, which form a part hereof, and in which is shown by way ofillustration specific implementations in which the disclosure may bepracticed. It is understood that other implementations may be utilizedand structural changes may be made without departing from the scope ofthe present disclosure. References in the specification to “oneembodiment,” “an embodiment,” “an example embodiment,” etc., indicatethat the embodiment described may include a particular feature,structure, or characteristic, but every embodiment may not necessarilyinclude the particular feature, structure, or characteristic. Moreover,such phrases are not necessarily referring to the same embodiment.Further, when a particular feature, structure, or characteristic isdescribed in connection with an embodiment, it is submitted that it iswithin the knowledge of one skilled in the art to affect such feature,structure, or characteristic in connection with other embodimentswhether or not explicitly described.

Implementations of the systems, devices, and methods disclosed hereinmay comprise or utilize a special purpose or general-purpose computerincluding computer hardware, such as, for example, one or moreprocessors and system memory, as discussed in greater detail below.Implementations within the scope of the present disclosure may alsoinclude physical and other computer-readable media for carrying orstoring computer-executable instructions and/or data structures. Suchcomputer-readable media can be any available media that can be accessedby a general purpose or special purpose computer system.Computer-readable media that store computer-executable instructions arecomputer storage media (devices). Computer-readable media that carrycomputer-executable instructions are transmission media. Thus, by way ofexample, and not limitation, implementations of the disclosure cancomprise at least two distinctly different kinds of computer-readablemedia: computer storage media (devices) and transmission media.

Computer storage media (devices) includes RAM, ROM, EEPROM, CD-ROM,solid state drives (“SSDs”) (e.g., based on RAM), Flash memory,phase-change memory (“PCM”), other types of memory, other optical diskstorage, magnetic disk storage or other magnetic storage devices, or anyother medium which can be used to store desired program code means inthe form of computer-executable instructions or data structures andwhich can be accessed by a general purpose or special purpose computer.

An implementation of the devices, systems, and methods disclosed hereinmay communicate over a computer network. A “network” is defined as oneor more data links that enable the transport of electronic data betweencomputer systems and/or modules and/or other electronic devices. Wheninformation is transferred or provided over a network or anothercommunications connection (either hardwired, wireless, or a combinationof hardwired or wireless) to a computer, the computer properly views theconnection as a transmission medium. Transmissions media can include anetwork and/or data links which can be used to carry desired programcode means in the form of computer-executable instructions or datastructures and which can be accessed by a general purpose or specialpurpose computer. Combinations of the above should also be includedwithin the scope of computer-readable media.

Computer-executable instructions comprise, for example, instructions anddata which, when executed at a processor, cause a general purposecomputer, special purpose computer, or special purpose processing deviceto perform a certain function or group of functions. The computerexecutable instructions may be, for example, binaries, intermediateformat instructions such as assembly language, or even source code.Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the described features or acts described above.Rather, the described features and acts are disclosed as example formsof implementing the claims.

Those skilled in the art will appreciate that the disclosure may bepracticed in network computing environments with many types of computersystem configurations, including, an in-dash computer, personalcomputers, desktop computers, laptop computers, message processors,hand-held devices, multi-processor systems, microprocessor-based orprogrammable consumer electronics, network PCs, minicomputers, mainframecomputers, mobile telephones, PDAs, tablets, pagers, routers, switches,various storage devices, and the like. The disclosure may also bepracticed in distributed system environments where local and remotecomputer systems, which are linked (either by hardwired data links,wireless data links, or by a combination of hardwired and wireless datalinks) through a network, both perform tasks. In a distributed systemenvironment, program modules may be located in both local and remotememory storage devices.

Further, where appropriate, functions described herein can be performedin one or more of: hardware, software, firmware, digital components, oranalog components. For example, one or more application specificintegrated circuits (ASICs) can be programmed to carry out one or moreof the systems and procedures described herein. Certain terms are usedthroughout the following description and Claims to refer to particularsystem components. As one skilled in the art will appreciate, componentsmay be referred to by different names. This document does not intend todistinguish between components that differ in name, but not function.

Referring now to the figures, FIG. 1 illustrates a vehicle controlsystem 100 that includes an automated driving/assistance system 102. Theautomated driving/assistance system 102 may be used to automate orcontrol operation of a vehicle or to provide assistance to a humandriver. For example, the automated driving/assistance system 102 maycontrol one or more of braking, steering, acceleration, lights, alerts,driver notifications, radio, or any other auxiliary systems of thevehicle. In another example, the automated driving/assistance system 102may not be able to provide any control of the driving (e.g., steering,acceleration, or braking), but may provide notifications and alerts toassist a human driver in driving safely. The automateddriving/assistance system 102 includes a speed component 104, which mayestimate a speed limit for a current location (e.g., based on sensordata at the current location) and may notify or alert a human driver orthe automated driving/assistance system 102 of the estimated speedlimit. For example, when no clear indication of a speed limit ispresent, the human driver or automated driving/assistance system 102 canuse the estimated speed limit as a guide.

The vehicle control system 100 also includes one or more sensorsystems/devices for detecting a presence of nearby objects ordetermining a location of a parent vehicle (e.g., a vehicle thatincludes the vehicle control system 100). For example, the vehiclecontrol system 100 may include one or more radar systems 106, one ormore LIDAR systems 108, one or more camera systems 110, a globalpositioning system (GPS) 112, and/or one or more ultrasound systems 114.The vehicle control system 100 may include a data store 116 for storingrelevant or useful data for navigation and safety such as a drivinghistory, map data, or other data. The vehicle control system 100 mayalso include a transceiver 118 for wireless communication with a mobileor wireless network, other vehicles, infrastructure, or any othercommunication system. The vehicle control system 100 may include vehiclecontrol actuators 120 to control various aspects of the driving of thevehicle such as electric motors, switches or other actuators, to controlbraking, acceleration, steering or the like. The vehicle control system100 may also include one or more displays 122, speakers 124, or otherdevices so that notifications to a human driver or passenger may beprovided. The display 122 may include a heads-up display, dashboarddisplay or indicator, a display screen, or any other visual indicator,which may be seen by a driver or passenger of a vehicle. The speakers124 may include one or more speakers of a sound system of a vehicle ormay include a speaker dedicated to driver notification.

It will be appreciated that the embodiment of FIG. 1 is given by way ofexample only. Other embodiments may include fewer or additionalcomponents without departing from the scope of the disclosure.Additionally, illustrated components may be combined or included withinother components without limitation. For example, the speed component104 may be separate from the automated driving/assistance system 102 andthe data store 116 may be included as part of the automateddriving/assistance system 102 and/or part of the speed component 104.

The radar system 106 may include any radar system well known in the art.Radar system 106 operation and performance is generally well understood.In general, a radar system 106 operates by transmitting radio signalsand detecting reflections off objects. In ground applications, the radarmay be used to detect physical objects, such as other vehicles, parkingbarriers or parking chocks, landscapes (such as trees, cliffs, rocks,hills, or the like), road edges, signs, buildings, or other objects. Theradar system 106 may use the reflected radio waves to determine a size,shape, distance, surface texture, or other information about a physicalobject or material. For example, the radar system 106 may sweep an areato obtain data about objects within a specific range and viewing angleof the radar system 106. In one embodiment, the radar system 106 isconfigured to generate perception information from a region near thevehicle, such as one or more regions nearby or surrounding the vehicle.For example, the radar system 106 may obtain data about regions of theground or vertical area immediately neighboring or near the vehicle. Theradar system 106 may include one of many widely available commerciallyavailable radar systems. In one embodiment, the radar system 106 mayprovide perception data including a two dimensional or three-dimensionalmap or model to the automated driving/assistance system 102 forreference or processing.

The LIDAR system 108 may include any LIDAR system known in the art.Principles of operation and performance of LIDAR systems are generallywell understood. In general, the LIDAR system 108 operates by emittingvisible wavelength or infrared wavelength lasers and detectingreflections of the laser light off objects. In ground applications, thelasers may be used to detect physical objects, such as other vehicles,parking barriers or parking chocks, landscapes (such as trees, cliffs,rocks, hills, or the like), road edges, signs, buildings, or otherobjects. The LIDAR system 108 may use the reflected laser light todetermine a size, shape, distance, surface texture, or other informationabout a physical object or material. For example, the LIDAR system 108may sweep an area to obtain data or objects within a specific range andviewing angle of the LIDAR system 108. For example, the LIDAR system 108may obtain data about regions of the ground or vertical area immediatelyneighboring or near the vehicle. The LIDAR system 108 may include one ofmany widely available commercially available LIDAR systems. In oneembodiment, the LIDAR system 108 may provide perception data including atwo dimensional or three-dimensional model or map of detected objects orsurfaces.

The camera system 110 may include one or more cameras, such as visiblewavelength cameras or infrared cameras. The camera system 110 mayprovide a video feed or periodic images, which can be processed forobject detection, road identification and positioning, or otherdetection or positioning. In one embodiment, the camera system 110 mayinclude two or more cameras, which may be used to provide ranging (e.g.,detect a distance) for objects within view.

The GPS system 112 is one embodiment of a positioning system that mayprovide a geographical location of the vehicle based on satellite orradio tower signals. GPS systems 112 are well known and widely availablein the art. Although GPS systems 112 can provide very accuratepositioning information, GPS systems 112 generally provide little or noinformation about distances between the vehicle and other objects.Rather, they simply provide a location, which can then be compared withother data, such as maps, to determine distances to other objects,roads, or locations of interest.

The ultrasound system 114 may be used to detect objects or distancesbetween a vehicle and objects using ultrasonic waves. For example, theultrasound system 114 may emit ultrasonic waves from a location on ornear a bumper or side panel location of a vehicle. The ultrasonic waves,which can travel short distances through air, may reflect off otherobjects and be detected by the ultrasound system 114. Based on an amountof time between emission and reception of reflected ultrasonic waves,the ultrasound system 114 may be able to detect accurate distancesbetween a bumper or side panel and any other objects. Due to its shorterrange, ultrasound systems 114 may be more useful to detect objectsduring parking or detect imminent collisions during driving.

In one embodiment, the radar system(s) 106, LIDAR system(s) 108, camerasystem(s) 110, and ultrasound system(s) 114 may detect environmentalattributers near a vehicle. For example, the systems 106-110 and 114 maydetect a number of lanes, lane width, shoulder width, road surfacecurvature, road direction curvature, rumble strips, lane markings,presence of intersections, road signs, bridges, overpasses, barriers,medians, curbs, or any other details about a road. As a further example,the systems 106-110 and 114 may detect environmental attributes thatinclude information about structures, objects, or surfaces near theroad, such as the presence of drive ways, parking lots, parking lotexits/entrances, sidewalks, walkways, trees, fences, buildings, parkedvehicles (on or near the road), gates, signs, parking strips, or anyother structures or objects.

The data store 116 stores map data, a driving history, and other data,which may include other navigational data, settings, or operatinginstructions for the automated driving/assistance system 102. The mapdata may include location data, such as GPS location data, for roads,parking lots, parking stalls, or other places where a vehicle may bedriven or parked. For example, the location data for roads may includelocation data for specific lanes, such as lane direction, merging lanes,highway or freeway lanes, exit lanes, or any other lane or division of aroad. The location data may also include locations for each parkingstall in a parking lot or for parking stalls along a road. In oneembodiment, the map data includes location data about one or morestructures or objects on or near the roads or parking locations. Forexample, the map data may include data regarding GPS sign location,bridge location, building or other structure location, or the like. Inone embodiment, the map data may include precise location data withaccuracy within a few meters or within sub meter accuracy. The map datamay also include location data for paths, dirt roads, or other roads orpaths, which may be driven by a land vehicle.

The driving history (or drive history) may include location data forpast trips or parking locations of the vehicle. For example, the drivinghistory may include GPS location data for the previous trips or pathstaken. As another example, the driving history may include distance orrelative location data with respect to lane lines, signs, road borderlines, or other objects or features on or near the roads. The distanceor relative location data may be determined based on GPS data, radardata, LIDAR data, camera data, or other sensor data gathered during theprevious or past trips taken by the vehicle. In one embodiment, theautomated driving/assistance system 102 is configured to log drivingdata to the data store 116 for and during any trips or drives taken bythe vehicle.

The transceiver 118 is configured to receive signals from one or moreother data or signal sources. The transceiver 118 may include one ormore radios configured to communicate according to a variety ofcommunication standards and/or using a variety of different frequencies.For example, the transceiver 118 may receive signals from othervehicles. Receiving signals from another vehicle is referenced herein asvehicle-to-vehicle (V2V) communication. In one embodiment, thetransceiver 118 may also be used to transmit information to othervehicles to potentially assist them in locating vehicles or objects.During V2V communication the transceiver 118 may receive informationfrom other vehicles about their locations, other traffic, accidents,road conditions, the locations of parking barriers or parking chocks, orany other details that may assist the vehicle and/or automateddriving/assistance system 102 in driving accurately or safely.

The transceiver 118 may receive signals from other signal sources thatare at fixed locations. Infrastructure transceivers may be located at aspecific geographic location and may transmit its specific geographiclocation with a time stamp. Thus, the automated driving/assistancesystem 102 may be able to determine a distance from the infrastructuretransceivers based on the time stamp and then determine its locationbased on the location of the infrastructure transceivers. In oneembodiment, receiving or sending location data from devices or towers atfixed locations is referenced herein as vehicle-to-infrastructure (V2X)communication. In one embodiment, the term V2X communication may alsoencompass V2V communication.

In one embodiment, the transceiver 118 may send and receive locationdata via a mobile network or cell connection. For example, thetransceiver 118 may receive updated location data for a specific area asa vehicle travels along a roadway. Similarly, the transceiver 118 mayreceive historical driving data for a parent vehicle or other vehiclesthat have driven along a road or parked in a parking lot at thatlocation or at that stall. For example, the transceiver 118 may receivedata that indicates locations of signs, parking barriers or parkingchocks, or other objects, which may be detectable using a radar system106, LIDAR system 108, camera system 110, GPS system 112, or ultrasoundsystem 114. If the transceiver 118 is able to receive signals from threeor more infrastructure transceivers, the automated driving/assistancesystem 102 may be able to triangulate its geographic location.

In one embodiment, the transceiver 118 may send and receive dataindicating a speed limit of a road, section of road, town, orgeographical area. In one embodiment, the transceiver 118 may receive anarbitrated speed limit that is provided by an authoritative source, suchas a specific reference, website, server, or sender. In one embodiment,the arbitrated speed limit may be a speed limit that has a highlikelihood of being correct and is interpreted by the automateddriving/assistance system 102 or speed component 104 as being ahigh-confidence legal speed limit.

In one embodiment, the automated driving/assistance system 102 isconfigured to control driving or navigation of a parent vehicle. Forexample, the automated driving/assistance system 102 may control thevehicle control actuators 120 to drive a path on a road, parking lot,driveway or other location. For example, the automateddriving/assistance system 102 may determine a path and speed to drivebased on information or perception data provided by any of thecomponents 106-118.

In one embodiment, the speed component 104 is configured to determine anestimated speed limit based on data gathered or provided by thecomponents 106-118. In one embodiment, the speed component 104 determinean estimated speed limit based on perception data captured by a radarsystem 106, LIDAR system 108, camera system 110, and/or an ultrasoundsystem 114.

FIGS. 2-4 are line drawings illustrating perception data comprisingimages captured by a camera system 110. The speed component 104 mayprocess the view(s) to identify attributes of environment depicted inthe perception data (e.g., image, LIDAR data, radar data, ultrasounddata, or other perception data). FIG. 2 is a perspective view of aresidential street environment 200. In one embodiment, the speedcomponent 104 may identify environmental attributes, which includevalues or objects such as a road width 202, one or more houses 204, oneor more trees 206, one or more parking strips 208, one or more curbs210, one or more driveways 212, sidewalks 214, or other attributes ofthe residential street environment 200. For example, the speed component104 may use one or more image processing algorithms (such as edgefinding, signature matching, or the like) to detect edges of objects orfeatures and identify objects or features in the residential streetenvironment 200. For example, the speed component 104 may determine aspeed limit based on determining that a vehicle is currently in aresidential environment. For example, the speed component 104 maydetermine that a speed limit is 25 MPH or 35 MPH, or less, in aresidential environment depending upon the attributes of the particularstreet environment 200.

The road width 202 may include a distance between edges of pavement,curbs, painted lines, or the like. The road width 202 may generally, butnot always, correspond positively with a higher speed limit. Forexample, more narrow roads may have lower speed limits, while widerroads may have higher speed limits. The houses 204 may includestructures or surrounding environments that likely correspond to aresidential house or home. For example, the presence of garage doors,walkways, sidewalks 214, periodic buildings, or structural detail andarchitecture usually corresponding to homes may be used to determinethat an identified structure is a house 204. Trees 206 may be detectedbased on color, signature patterns of leaves, height, branches,movement, or the like. Parking strips 208 may be detected based onlocation near a road, placement between the road and a sidewalk 214, orthe like. Curbs 210 may be detected based on color, height, locationnear a road, position between the road and other surface features, orthe like. Driveways 212 may be identified based on the presence of oneor more of a lack of parking strip 208, similar color and textureextending to the road, the presence of garage doors, parked vehicles, orthe like. Sidewalks 214 may be detected based on color, surfacesmoothness or texture, position near a road, contrast between nearbyterrain, presence of pedestrians, or the like.

The features or attributes 202-214 discussed are given by way of exampleonly. For example, additional attributes, which may be detected inresidential environments, may include cars parked on or near the street,the presence of pedestrians, children, animals, pets, residentiallandscaping features such as flowers and bushes, or any other aspect. Insome situations, machine learning algorithms may identify additionalfeatures or attributes based on images or perception data. For example,distances from a road to structures, lack of signs, presence of housenumbering, mailboxes, color schemes, or any other environmentalattributes may be identified by a speed learning algorithm, which mayhave not been specifically coded for or recognized by a human.

FIG. 3 is a perspective view of a commercial or industrial streetenvironment 300. In one embodiment, the speed component 104 may identifyenvironmental attributes, which include values or objects such as laneor road markings 302, lane width 304, a number of lanes, color of lanemarkings to determine direction of traffic or the presence ofbi-directional traffic, gutters 306, curbs 308, parking strips 310,sidewalks 312, large signs 314, intersections or intersecting roads 316,large or commercial buildings 318, vehicles 320, trees 322, bushes 324,or the like. For example, the speed component 104 may determine a speedlimit based on determining that a vehicle is currently in a commercial,industrial or other street environment. For example, the speed component104 may determine that a speed limit is 35 MPH or 45 MPH, or less, in acommercial or industrial environment based on the environmentalattributes.

The road markings 302 may be identified based on contrasted color with aroad surface, shape (e.g., long and skinny), presence on the road, orthe like. The road markings 302 may be used to determine a lane width304, a number of lanes, lane direction, and/or the presence ofdirectional or bi-directional traffic. Gutters 306 may be detected basedon color, slope, location near a road or curb 308, or the like. Signs314 may be detected or identified based on size, lettering size andstyle, illumination, shape, or the like. Intersections or intersectingroads 316 may be detected based on position in relation to the road,width, or the like. Commercial, industrial, or public buildings, such asthe building 318, may be detected based on size, architecture, style,building size, signage, proximity to a road or parking lot, or the like.In one embodiment, the presence of the objects or features 302-324, andtheir positional relationships with regard to a road may be used todetermine that a road is in a commercial environment or that the roadhas a specific speed limit.

FIG. 4 is a perspective view of an interstate highway street environment400. In one embodiment, the speed component 104 may identifyenvironmental attributes, which include values or objects such as roadboundary markings 402 (e.g., painted lines), lane markings 404, rightshoulder width 406, rumble strips 408, left shoulder width 410, trees412 and shrubbery 414, signs 416, street lights 418, or the like. Forexample, the speed component 104 may determine a speed limit based ondetermining that a vehicle is currently in an interstate highwayenvironment. For example, the speed component 104 may determine that aspeed limit is 55 or 65 MPH, or more, on an interstate highway. In oneembodiment, a curvature of the road may also be detected to determinethat a reduced or temporarily reduced speed limit is in effect.

The presence and color of the road boundary markings 402 and lanemarkings 404 may be used to determine a direction of travel for one ormore lanes, the number of lanes, or the like. For example, if theleft-most road boundary marking 402 depicted in the figure is yellow,then the speed component 104 may determine that all three lanes in theinterstate highway street environment 400 are going in the samedirection. As another example, if both of the road boundary markings 402are white and one row of the lane markings 404 is yellow, the speedcomponent 104 may determine that the vehicle is in a bi-directionaltraffic environment. The size of the shoulder 406 and the rumble strips408 may indicate that a higher speed limit, such as 55 MPH, 65 MPH, or75 MPH in the United States, is available. Furthermore, the presence andsize of a left shoulder 410, the amount of trees 412 and shrubbery 414,and lack of buildings may also indicate that a higher speed limit isavailable. The size, shape, and wording on the sign 416, such as streetsigns, even if no speed limit is indicated, may also indicate that theroad is an interstate road.

The environmental attributes discussed above are given by way of exampleonly. For example, any additional environmental attributes may affect aspeed limit. In one embodiment, the presence of school zone signs,flashing lights, or the like may indicate that a slower speed limit isrequired. In one embodiment, the presence of traffic cones, constructionbarriers, or the like may also indicate a reduced speed limit. In oneembodiment, the speed component 104 may use environmental attributes asweighted factors indicating specific speed limits and/or zonedenvironments (e.g., residential, industrial, commercial, wilderness,highway, freeway, interstate, school zone, etc.). In one embodiment, agraphical model may be used to combine the presence, lack of presence,or the values of a current environment of a vehicle to determine acurrent estimated speed.

Although some examples herein are given with respect to camera images,other sensor data may also be used to acquire perception data anddetermine attributes of a current road or driving environment. Forexample, in one embodiment, a LIDAR system 108, radar system 106, and/orultrasound system 114 may gather information about the environment andmay be processed by the speed component 104 to estimate a current speedlimit.

FIG. 5 is a schematic top view of a road 500 with a vehicle 502traveling on the road. The vehicle 502 may include the system 100 ofFIG. 1. In one embodiment, one or more sensors, such as a camera system110 of the vehicle may have a viewing area 504 indicated by dottedlines. The viewing area 504 is illustrative only and may extend in anydirection or all directions around the vehicle 502. The vehicle 502, ora speed component 104 of the vehicle 502, may receive perception datafrom objects, surfaces, or the like within the viewing area 504 andestimate a current speed limit. In one embodiment, the presence, orabsence, of certain objects or environmental attributes may bedetermined by the vehicle 502 based on the data gathered regarding theviewing area 504.

In one embodiment, the vehicle 502 may process perception data to detectthe presence of another vehicle 508, signs 510 or 512, an intersectingroad 514, or any other environmental attributes discussed in relation toFIGS. 2-4 or elsewhere in the disclosure. Based on the detected objectsor attributes, the vehicle 502 may be able to determine an estimatedspeed limit or an actual speed limit. For example, the sign 512 may be aspeed limit sign that states an actual speed limit for the road 500. Ifa known speed limit can be detected or identified, the vehicle 502 mayuse perception data for machine learning or training of an estimationmodel to provide such known speed limit information to the vehicle 502.The term “known speed limit” may also be referred to herein as ahigh-confidence speed limit, arbitrated speed limit, or actual legalspeed limit. In one embodiment, the sign 512 may not be a speed limitsign and may instead by a welcome sign, road identification sign, or anyother sign. Although a known speed limit or arbitrated speed limit maynot be derived from the sign, information on the sign may be used incombination with other environmental attributes to determine anestimated speed limit. For example, the sign may include a roadidentifier, which may indicate whether the road is an interstate road,city road, state road, wilderness road, private road, or any other typeof road. The road identifier may be used in combination with thepresence of an intersecting road 514 or other environmental attributesto estimate a current speed limit for the vehicle 502.

In addition to perception data, the vehicle 502 may obtain informationfrom a stored map, stored driving history, or from wireless signals. Forexample, an infrastructure transmitter 506 is shown near the road, whichmay provide specific positioning, environmental attribute details, orother information to the vehicle. As further examples, the vehicle 502may receive information from other vehicles, such as vehicle 508, orfrom a wireless communication network, such as a wireless communicationnetwork.

FIG. 6 is a schematic block diagram illustrating components of a speedcomponent 104, according to one embodiment. The speed component 104includes a perception data component 602, an attribute component 604, anestimator component 606, a notification component 608, an arbitratedspeed component 610, and a learning component 612. The components602-612 are given by way of illustration only and may not all beincluded in all embodiments. In fact, some embodiments may include onlyone or any combination of two or more of the components 602-612. Some ofthe components 602-612 may be located outside the speed component 104,such as within the automated driving/assistance system 102 or elsewhere.

The perception data component 602 is configured to receive perceptiondata from one or more perception sensors that generate perception dataabout a region near a vehicle. The perception data may include dataproduced by one or more of a camera system 110, radar system 106, LIDARsystem 108, and ultrasound system 114. In one embodiment, the one ormore perception sensors include a camera and the perception datacomponent 602 receives an image capture by the camera.

The attribute component 604 is configured to process perception data todetect one or more environmental attributes of an area near a vehiclebased on the perception data. For example, the attribute component mayperform object recognition, pattern recognition, object or surface edgedetection, or other recognition to detect, calculate, identify, orotherwise determine the environmental attributes. In one embodiment, theattributes includes one or more physical attributes or features of aroad, structure, or object in the region near the vehicle. For example,the attributes may include one or more of a size of a shoulder of aroad, a number of lanes on a road, a presence of houses , a presence ofbuildings, a presence of parked cars, a presence of a concrete medium, alane marking, a curb, a rumble strip, a lane width, and a roadcurvature. Similarly, the attributes may include the presence ofinterrupted and/or periodic buildings, driveways, walkways, cars parkedin driveways, or the like. The attribute component 604 may detect anyattribute discussed herein or additional attributes that may bediscovered or identified by a machine learning algorithm.

In one embodiment, the perception data may include image or videocaptured by one or more cameras. The attribute component 604 may processthe image or video to determine or identify the one or moreenvironmental attributes. In one embodiment, for example, the attributecomponent 604 may determine attributes in the environment solely basedupon one or more images. Similarly, the attribute component 604 maydetermine the attributes solely based upon sensor data from one or moreof camera systems 110, LIDAR systems 108, radar systems 106, andultrasound systems 114.

The estimator component 606 is configured to determine an estimatedspeed limit based on the environmental attributes. For example, theestimator component 606 may determine an estimated speed limit for anarea near the vehicle based on environmental attributes determined bythe attribute component 604. In one embodiment, the estimator component606 is configured to determine the estimated speed limit based on anestimation model or a graphical model. The estimation model or graphicalmodel may associate at least one of the one or more environmentalattributes with a speed limit or an indication to adjust a speed limit.For example, certain attributes may correlate positively with anincreased speed limit, while other attributes may correlate negativelywith an increased speed limit. As another example, a first combinationof certain attributes may indicate a specific speed limit, whilecombinations of other attributes, which may include attributes in commonwith the first combination, may indicate a different speed limit.Similarly, a graphical model may be traversed to arrive at an estimatedspeed limit.

In one embodiment, the estimator component 606 may determine a possiblerange of speed limits for the current environment. In one embodiment,the estimator component 606 may determine a speed limit at a lower endof a determined range to limit chances that the vehicle will exceed alegal speed limit. On the other hand, the estimator component 606 maydetermine a speed limit on an upper end to reduce drive time.

The notification component 608 is configured to provide an indication ofthe estimated speed limit to a human driver or automated driving system,such as 102, of the vehicle 502. For example, if a human driver isdriving the vehicle 502, a visual or audio notification of the estimatedspeed limit may be provided within the cab of the vehicle 502. In oneembodiment, the notification component 608 may provide an indication bysending a signal or message to the automated driving system 102 thatindicates the estimated speed limit. In one embodiment, the notificationcomponent 608 provides an indication that the estimated speed limit isan estimate, and not the actual speed limit. Based on the estimatedspeed limit and/or the indication that it is an estimate, a human driveror automated driving system 102 may be able to determine a properdriving path, speed, or the like. For example, a human driver orautomated driving system 102 may determine that the estimate is too lowor too high and modify actual driving speed accordingly. In oneembodiment, the notification component 608 may provide the estimatedspeed limit to a learning component 612 or arbitrated speed component610.

The arbitrated speed component 610 is configured to attempt to determinea known or actual speed limit with high-confidence. For example, thearbitrated speed component 610 may determine a speed limit based on atrusted source or trusted indication of the speed limit, such as a speedlimit sign (detected using a camera or other sensor), a map, anelectronic horizon system that provides predictive driver assistance,data from other third party systems or networks, or the like. Anelectronic horizon system may provide map data for features such asnight vision, curve speed warning, traffic sign detection, speed limitsfor the road, or the like.

In one embodiment, the arbitrated speed component 610 is configured todetermine the arbitrated speed limit based on information provided by athird party. For example, the data provided by a third party may includea road sign detected by the one or more sensors, map data stored by thesystem, a vehicle-to-vehicle communication, an infrastructure-to-vehiclecommunication, and/or data received via a mobile network. In oneembodiment, the arbitrated speed component 610 may determine thearbitrated speed limit based on a current location of the vehicle inresponse to determine a GPS location, map location, or other location.In one embodiment, the arbitrated speed component 610 may determine ahigh-confidence speed limit based on perception data gathered by one ormore sensors. For example, the arbitrated speed component 610 mayperform optical character recognition on an image of a sign captured bya camera system 110 to recognize that the sign is a speed limit sign anddetermine the posted speed.

In one embodiment, the arbitrated speed component 610 may track anamount of time or a distance or change in roads to determine whether ahigh-confidence speed limit is known for a current location of avehicle. For example, the arbitrated speed component 610 may determinethat an arbitrated speed limit has not been determined for at least athreshold time or distance. As another example, the arbitrated speedcomponent 610 may determine that an arbitrated speed limit has not beendetermined for a current road or region of the current road.

In one embodiment, the arbitrated speed component 610 may be unable todetermine a current speed limit for a period of time. During this periodof time, a vehicle driving system or vehicle assistance system 102 mayestimate a speed limit based on perception data and control drivingbased on the estimated speed limit or notify a driver of the estimatedspeed limit. At a later point, the arbitrated speed component 610 maydetermine an arbitrated speed limit and determine that the arbitratedspeed limit corresponds to a location of the vehicle 502 during theperiod of time where no arbitrated speed limit was known. The arbitratedspeed limit may be stored in a driving history for later access by thearbitrated speed component 610 in order to enable an arbitrated speedlimit to be determined sooner on a subsequent trip. Similarly, thearbitrated speed limit may be used to train speed limit estimation bythe speed component 104.

The learning component 612 is configured to perform machine learning toupdate a machine learning algorithm, model, function or database. In oneembodiment, the learning component 612 is configured to update theestimation model to update or create an association between one or moreenvironmental attributes and an estimated speed limit based on ahigh-confidence speed limit. For example, a database or model may storeor represent a plurality of associations between environmentalattributes and an estimated speed limit. Based on the presence of theenvironmental attributes, a specific speed limit may be indicated by thedatabase, a graphical model, or any other data structure or algorithm.In one embodiment, the learning component 612 may detect a plurality ofenvironmental attributes in perception data associated with an areahaving an arbitrated speed limit and associate the environmentalattributes with the arbitrated speed limit. The environmental attributesand a speed limit may be associated with weighted averages, by groups,or by any other method. For example, a given set of environmentalattributes may indicate a first speed limit with a first percentage orconfidence level and also indicate a second speed limit with a secondpercentage or confidence level. The estimator component 606 may selectthe highest percentage or highest confidence level value, or may selectthe lower (or a middle or other value if there is more than two) of thetwo as the estimated speed limit.

In one embodiment, the learning component 612 may be trained using speedlimits images, or other perception data even before a vehicle is used sothat the speed component 104 has a base understanding of speed limitestimation. According to one embodiment, during further use of thevehicle, the learning component 612 may update a learning algorithm ormodel as the vehicle is driven through known and unknown speed limitareas based on gathered data, speed limits, data downloads, or any othermethod to improve performance or accuracy of speed limit estimation.

FIG. 7 is a schematic block diagram illustrating a model for speed limitestimation, according to one embodiment. Perception data including oneor more of camera data, radar data, LIDAR data, and ultrasound data maybe provided to a speed limit estimator (such as a speed component 104and/or estimator component). The speed limit estimator estimates a speedlimit based at least in part or solely on the perception data andprovides the estimated speed limit to an automated driving system,driving assistances system, or the like.

FIG. 8 is a schematic block diagram illustrating a model for speed limitestimation and machine learning, according to one embodiment. Perceptiondata including one or more of camera data, radar data, LIDAR data, andultrasound data may be provided to a speed limit estimator (such as aspeed component 104 and/or estimator component). The speed limitestimator estimates a speed limit based at least in part or solely onthe perception data and provides the estimated speed limit to anautomated driving system, driving assistances system, or the like. If ahigh-confidence speed limit or arbitrated speed limit is available, alearning algorithm may update the speed limit estimator (such as adatabase, model, function, or map) based on the current perception dataand the current arbitrated speed limit.

FIG. 9 is a schematic flow diagram illustrating a method for determiningwhether to use an estimated speed limit or arbitrated speed limit (orknown speed limit). The method 900 begins and the speed component 104determines, at 902, whether a high-confidence speed limit is available.For example, the speed component 104 may determine whether a legal speedlimit can be determined based on a perceived posted speed limit (speedlimit sign), a map, an electronic horizon system, or other source of ahigh-confidence or arbitrated speed limit. If there is nohigh-confidence speed limit, the speed component 104 estimates a speedlimit based on data from one or more perception sensors. If there is ahigh-confidence speed limit, the high-confidence speed limit may be usedand an estimator (such as the estimator component 606) is trained basedon the high-confidence speed limit and any available perception data foran area corresponding to the high-confidence speed limit.

Referring now to FIG. 10, a schematic flow chart diagram of a method1000 for determining a speed limit is illustrated. The method 1000 maybe performed by an automated driving/assistance system or a speedcomponent, such as the automated driving/assistance system 102 of FIG. 1or the speed component 104 of FIG. 1 or 6. For example, the method 1000may correspond to estimating the speed limit using the perceptionsensors in FIG. 9.

The method 1000 begins as an attribute component 604 detects at 1002 oneor more environmental attributes based on the perception data. Anestimator component 606 determines at 1004 an estimated speed limitbased on the environmental attributes. A notification component 608provides at 1006 the estimated speed limit to an automated drivingsystem or driver assistance system of the vehicle.

EXAMPLES

The following examples pertain to further embodiments.

Example 1 is a system that includes one or more perception sensors, anattribute component, an estimator component, and a notificationcomponent. The one or more perception sensors are configured to generateperception data about a region near a vehicle. The attribute componentis configured to detect one or more environmental attributes based onthe perception data. The estimator component is configured to determinean estimated speed limit based on the environmental attributes. Thenotification component is configured to provide the estimated speedlimit to an automated driving system or driver assistance system of thevehicle.

In Example 2, the one or more environmental attributes in Example 1include one or more physical attributes or features of a road,structure, or object in the region near the vehicle.

In Example 3, attribute component in any of Examples 1-2 detect one ormore environmental attributes that include one or more of a size of ashoulder of a road, a number of lanes on a road, a presence of houses, apresence of buildings, a presence of parked cars, a presence of aconcrete medium, a lane marking, a curb, a rumble strip, a lane width,and a road curvature.

In Example 4, the estimator component in any of Examples 1-3 isconfigured to determine the estimated speed limit based on an estimationmodel, wherein the estimation model associates at least one of the oneor more environmental attributes with a speed limit or an indication toadjust a speed limit.

In Example 5, the one or more perception sensors in any of Examples 1-4include a camera and wherein the attribute component is configured toprocess an image from the camera to determine the one or moreenvironmental attributes.

In Example 6, any of Examples 1-5 further include an arbitrated speedcomponent configured to determine a high-confidence speed limit.

In Example 7, the Example of 6 further includes a learning componentconfigured to update an estimation model to update or create anassociation between the one or more environmental attributes and anestimated speed limit based on the high-confidence speed limit.

In Example 8, the arbitrated speed limit component in any of Examples6-7 further determines the high-confidence speed limit based on dataprovided by a third party.

In Example 9, the data provided by a third party in any of Examples 6-8includes a road sign detected by the one or more sensors, map datastored by the system, a vehicle-to-vehicle communication, aninfrastructure-to-vehicle communication, and data received via a mobilenetwork.

Example 10 is a method for machine learning of speed limits. The methodincludes receiving perception data from one or more perception sensors.The method includes receiving an indication of an arbitrated speed limitfor a location corresponding to the perception data. The method includesidentifying one or more environmental attributes based on the perceptiondata, wherein the attributes comprise one or more physical attributes orfeatures of a road, structure, or object near the road. The methodincludes associating the one or more environmental attributes with thearbitrated speed limit in an estimation model.

In Example 11, the one or more environmental attributes in Example 10include one or more of a size of a shoulder of a road, a number of laneson a road, a presence of houses, a presence of buildings, a presence ofparked cars, a presence of a concrete medium, a lane marking, a curb, arumble strip, a lane width, and a road curvature.

In Example 12, the location corresponding to the perception data in anyof Examples 10-11 includes a first location, wherein the method furtherincludes receiving perception data for a second location and determiningan estimated speed limit for the second location based on the estimationmodel.

In Example 13, the one or more perception sensors in any of Examples10-12 include a camera and the perception data comprises an imagecapture by the camera, wherein identifying the one or more environmentalattributes comprises processing the image to determine the one or moreenvironmental attributes.

In Example 14, the arbitrated speed limit in any of Examples 10-13 isreceived in data provided by a third party.

In Example 15, the data provided by a third party in Example 14 includesa road sign detected by the one or more sensors, map data stored by thesystem, a vehicle-to-vehicle communication, an infrastructure-to-vehiclecommunication, and data received via a mobile network.

Example 16 is a computer readable storage media storing instructionsthat, when executed by one or more processors, cause the processors toprocess perception data to detect one or more environmental attributesof an area near a vehicle based on the perception data. The instructionscause the processor to estimate a speed limit for the area near thevehicle based on the environmental attributes. The instructions causethe processor to notify an automated driving system or driver of thevehicle of the estimated speed limit.

In Example 17, the instructions in Example 16 further cause theprocessor to determine that one or more of an arbitrated speed limit hasnot been determined for at least a threshold time or distance and anarbitrated speed limit has not been determined for a current road orregion of the current road.

In Example 18, the one or more environmental attributes in any ofExamples 16-17 include one or more physical attributes or features of aroad, structure, or object in the region near the vehicle.

In Example 19, the perception data in any of Examples 16-18 include animage from a camera and wherein processing the perception data includesprocessing an image from the camera to determine the one or moreenvironmental attributes.

In Example 20, the instructions in any of Examples 16-19 further causethe processor to determine a high-confidence speed limit for the areaand to update an estimation model to update an association between theone or more environmental attributes and an estimated speed limit basedon the high-confidence speed limit.

It should be noted that the sensor embodiments discussed above maycomprise computer hardware, software, firmware, or any combinationthereof to perform at least a portion of their functions. For example, asensor may include computer code configured to be executed in one ormore processors, and may include hardware logic/electrical circuitrycontrolled by the computer code. These example devices are providedherein purposes of illustration, and are not intended to be limiting.Embodiments of the present disclosure may be implemented in furthertypes of devices, as would be known to persons skilled in the relevantart(s).

Embodiments of the disclosure have been directed to computer programproducts comprising such logic (e.g., in the form of software) stored onany computer useable medium. Such software, when executed in one or moredata processing devices, causes a device to operate as described herein.

While various embodiments of the present disclosure have been describedabove, it should be understood that they have been presented by way ofexample only, and not limitation. It will be apparent to persons skilledin the relevant art that various changes in form and detail can be madetherein without departing from the spirit and scope of the disclosure.Thus, the breadth and scope of the present disclosure should not belimited by any of the above-described exemplary embodiments, but shouldbe defined only in accordance with the following claims and theirequivalents. The foregoing description has been presented for thepurposes of illustration and description. It is not intended to beexhaustive or to limit the disclosure to the precise form disclosed.Many modifications and variations are possible in light of the aboveteaching. Further, it should be noted that any or all of theaforementioned alternate implementations may be used in any combinationdesired to form additional hybrid implementations of the disclosure.

Further, although specific implementations of the disclosure have beendescribed and illustrated, the disclosure is not to be limited to thespecific forms or arrangements of parts so described and illustrated.The scope of the disclosure is to be defined by the claims appendedhereto, any future claims submitted here and in different applications,and their equivalents.

What is claimed is:
 1. A system comprising: one or more perceptionsensors configured to generate perception data about a region near avehicle; an attribute component configured to detect one or moreenvironmental attributes based on the perception data; an estimatorcomponent configured to determine an estimated speed limit based on theenvironmental attributes; and a notification component configured toprovide the estimated speed limit to an automated driving system ordriver assistance system of the vehicle.
 2. The system of claim 1,wherein the one or more environmental attributes comprise one or morephysical attributes or features of a road, structure, or object in theregion near the vehicle.
 3. The system of claim 1, wherein the attributecomponent detects one or more environmental attributes comprising one ormore of: a size of a shoulder of a road, a number of lanes on a road, apresence of houses, a presence of buildings, a presence of parked cars,a presence of a concrete medium, a lane marking, a curb, a rumble strip,a lane width, and a road curvature.
 4. The system of claim 1, whereinthe estimator component is configured to determine the estimated speedlimit based on an estimation model, wherein the estimation modelassociates at least one of the one or more environmental attributes witha speed limit or an indication to adjust a speed limit.
 5. The system ofclaim 1, wherein the one or more perception sensors comprise a cameraand wherein the attribute component is configured to process an imagefrom the camera to determine the one or more environmental attributes.6. The system of claim 1, further comprising an arbitrated speedcomponent configured to determine a high-confidence speed limit.
 7. Thesystem of claim 6, further comprising a learning component configured toupdate an estimation model to update an association between the one ormore environmental attributes and an estimated speed limit based on thehigh-confidence speed limit.
 8. The system of claim 6, wherein thearbitrated speed limit component determines the high-confidence speedlimit based on data provided by a third party.
 9. The system of claim 6,wherein the data provided by a third party comprises a road signdetected by the one or more sensors, map data stored by the system, avehicle-to-vehicle communication, an infrastructure-to-vehiclecommunication, and data received via a mobile network.
 10. A method formachine learning of speed limits, the method comprising: receivingperception data from one or more perception sensors; receiving anindication of an arbitrated speed limit for a location corresponding tothe perception data; identifying one or more environmental attributesbased on the perception data, wherein the attributes comprise one ormore physical attributes or features of a road, structure, or objectnear the road; and associating the one or more environmental attributeswith a value of the arbitrated speed limit within an estimation model.11. The method of claim 10, wherein the one or more environmentalattributes comprise one or more of: a size of a shoulder of a road, anumber of lanes on a road, a presence of houses, a presence ofbuildings, a presence of parked cars, a presence of a concrete medium, alane marking, a curb, a rumble strip, a lane width, and a roadcurvature.
 12. The method of claim 10, wherein the locationcorresponding to the perception data comprises a first location, whereinthe method further comprises: receiving perception data for a secondlocation; and determining an estimated speed limit for the secondlocation based on the estimation model.
 13. The method of claim 10,wherein the one or more perception sensors comprise a camera and theperception data comprises an image capture by the camera, whereinidentifying the one or more environmental attributes comprisesprocessing the image to determine the one or more environmentalattributes.
 14. The method of claim 10, wherein the arbitrated speedlimit is received in data provided by a third party.
 15. The method ofclaim 14, wherein the data provided by a third party comprises a roadsign detected by the one or more sensors, map data stored by the system,a vehicle-to-vehicle communication, an infrastructure-to-vehiclecommunication, and data received via a mobile network.
 16. Computerreadable storage media storing instructions that, when executed by oneor more processors, cause the processors to: process perception data todetect one or more environmental attributes of an area near a vehiclebased on the perception data; estimate a speed limit for the area nearthe vehicle based on the environmental attributes; and notify anautomated driving system or driver of the vehicle of the estimated speedlimit.
 17. The computer readable storage media of claim 16, wherein theinstructions further cause the processor to determine that one or moreof: an arbitrated speed limit has not been determined for at least athreshold time or distance; and an arbitrated speed limit has not beendetermined for a current road or region of the current road.
 18. Thecomputer readable storage media of claim 16, wherein the one or moreenvironmental attributes comprise one or more physical attributes orfeatures of a road, structure, or object in the region near the vehicle.19. The computer readable storage media of claim 16, wherein theperception data comprise an image from a camera and wherein processingthe perception data comprise processing an image from the camera todetermine the one or more environmental attributes.
 20. The computerreadable storage media of claim 16, wherein the instructions furthercause the processor to determine a high-confidence speed limit for thearea and to update an estimation model to update an association betweenthe one or more environmental attributes and an estimated speed limitbased on the high-confidence speed limit.